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Advanced Hybrid Information Processing. 6th EAI International Conference, ADHIP 2022, Changsha, China, September 29-30, 2022, Proceedings, Part I

Research Article

Real Time Monitoring Method of Exercise Load in Dance Training Course Based on Intelligent Device

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-28787-9_9,
        author={XiaoSha Sun and Xiang Gao},
        title={Real Time Monitoring Method of Exercise Load in Dance Training Course Based on Intelligent Device},
        proceedings={Advanced Hybrid Information Processing. 6th EAI International Conference, ADHIP 2022, Changsha, China, September 29-30, 2022, Proceedings, Part I},
        proceedings_a={ADHIP},
        year={2023},
        month={3},
        keywords={Intelligent equipment Dance training Exercise load Exercise monitoring},
        doi={10.1007/978-3-031-28787-9_9}
    }
    
  • XiaoSha Sun
    Xiang Gao
    Year: 2023
    Real Time Monitoring Method of Exercise Load in Dance Training Course Based on Intelligent Device
    ADHIP
    Springer
    DOI: 10.1007/978-3-031-28787-9_9
XiaoSha Sun1,*, Xiang Gao1
  • 1: Sanmenxia Polytechnic
*Contact email: sunxiaosha_1984@126.com

Abstract

Because of the heavy exercise load in dance training class, students’ bearing capacity is different. In order to better scientifically guide the movement of dance training classes, a real-time monitoring method of exercise load in dance training classes based on smart devices is proposed. Combined with smart devices, it collects and manages a large number of physical fitness indicators of dance training, and uses heart rate changes as load evaluation indicators. Real-time acquisition of the movement status of dance training courses and real-time monitoring. Finally, it is confirmed by experiments that the real-time monitoring method of exercise load in dance training courses based on intelligent equipment has high practicability in practical application, so as to meet the research requirements.

Keywords
Intelligent equipment Dance training Exercise load Exercise monitoring
Published
2023-03-22
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-28787-9_9
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